Dynamic resource location in peer-to-peer networks

Resource location is a necessary operation for computer applications. In large scale peer-to-peer systems, random search is a scalable approach for locating dynamic resources. Current peer-to-peer systems can be partitioned into those which rely upon the Internet for message routing and those which utilize an overlay network. These two approaches result in different connectivity topologies. This thesis analyzes the effect of topological differences on the effectiveness of random search. After demonstrating the benefits of an overlay network, we propose a hybrid approach for resource location. Our proposed protocol provides deterministic searching capabilities which can help prevent request failures for sensitive applications

Dynamic resource location in peer-to-peer networks

Resource location is a necessary operation for computer applications. In large scale peer-to-peer systems, random search is a scalable approach for locating dynamic resources. Current peer-to-peer systems can be partitioned into those which rely upon the Internet for message routing and those which utilize an overlay network. These two approaches result in different connectivity topologies. This thesis analyzes the effect of topological differences on the effectiveness of random search. After demonstrating the benefits of an overlay network, we propose a hybrid approach for resource location. Our proposed protocol provides deterministic searching capabilities which can help prevent request failures for sensitive applications

Prevalence and Determinants of Long COVID among the COVID-19 Survivors: A Cross-sectional Study from A Rural Area of Maharashtra

Background: Most patients infected with the COVID-19 virus may experience long-term effects from COVID-19 infection, known as post-COVID or long COVID conditions. Long COVID may last for weeks, months or years and may limit ones day to day activities and needs health care. Aim & Objective: To study the prevalence and risk factors of long COVID among the COVID-19 survivors of a rural area of Maharashtra. Methods and Material: A Community-based cross-sectional study was conducted in adult subjects residing in Chanai village from Maharashtra who have had a history of COVID-19 and have passed more than 3 months since the diagnosis from May 2022 to June 2022. The interview of the study participants was conducted with the help of a pre-designed, semi-structured questionnaire for data collection. Statistical Analysis: Data was analyzed using Microsoft Excel 2010, Open EPI-Info version 3.01 updated on 2013/04/06. Data was presented in tables, graphical format, frequencies and percentages and the statistical association was shown using the chi- square test. Results: The majority of participants were males (59%), from 19 to 39 years of age group (57%), having fever as presenting symptom (83%), with mild COVID (13%), and required hospitalization (53%). Long COVID was associated with the elderly age group, male sex (27.1%), severe COVID presentation (88.2%) after 12 weeks, and those required intubation (80%). Conclusion: The prevalence of long COVID was 17.5%. Determinants associated with long COVID were the elderly age group, male sex, severe COVID presentation and who required intubation

Energy-Efficient Virtual Machine Placement using Enhanced Firefly Algorithm

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing

Mechanisms for coordinated power management with application to cooperative distributed systems

Computing systems are experiencing a significant evolution triggered by the convergence of multiple technologies including multicore processor architectures, expanding I/O capabilities (e.g., storage and wireless communication), and virtualization solutions. The integration of these technologies has been driven by the need to deliver performance and functionality for applications being developed in emerging mobile and enterprise systems. These accomplishments, though, have come at the cost of increased power and thermal signatures of computing platforms. In response to the resulting power issues, power centric policies have been deployed across all layers of the stack including platform hardware, operating systems, application middleware, and virtualization components. Effective active power management requires that these independent layers or components behave constructively to attain globally desirable benefits. Two choices are (1) to tightly integrate different policies using negotiated management decisions, and (2) to coordinate their use based on the localized policy decisions that are already part of modern computer architectures and software systems. Recognizing the realities of (2), the goal of this thesis is to identify, define, and evaluate novel system-level coordination mechanisms between diverse management components that exist across system layers. The end goal of these mechanisms, then, is to enable synergistic behaviors between management entities, across different levels of abstraction, and across different physical platforms to improve power management functionality. Contributions from this work include operating system level mechanisms that dynamically capture workload behavior thereby enabling power efficient scheduling, and system descriptor mechanisms that allow for improved workload allocation and resource management schemes. Finally, observing the strong need for coordination in managing virtualized systems due to the existence of multiple, independent system layers, a set of extensions to virtualization architectures for effectively coordinating VM management in datacenters are developed.Ph.D.Committee Chair: Schwan, Karsten; Committee Co-Chair: Yalamanchili, Sudha; Committee Member: Lee, Hsien-Hsin Sean; Committee Member: Loh, Gabriel; Committee Member: Madisetti, Vijay; Committee Member: Owen, Henr

Exploiting platform heterogeneity for power efficient data centers

It has recently become clear that power management is of critical importance in modern enterprise computing environments. The traditional drive for higher performance has influenced trends towards consolidation and higher densities, artifacts enabled by virtualization and new small form factor server blades. The resulting effect has been increased power and cooling requirements in data centers which elevate ownership costs and put more pressure on rack and enclosure densities. To address these issues, in this paper, we enable power-efficient management of enterprise workloads by exploiting a fundamental characteristic of data centers: “platform heterogeneity”. This heterogeneity stems from the architectural and management-capability variations of the underlying platforms. We define an intelligent workload allocation method that leverages heterogeneity characteristics and efficiently maps workloads to the best fitting platforms, significantly improving the power efficiency of the whole data center. We perform this allocation by employing a novel analytical prediction layer that accurately predicts workload power/performance across different platform architectures and power management capabilities. This prediction infrastructure relies upon platform and workload descriptors that we define as part of our work. Our allocation scheme achieves on average 20 % improvements in power efficiency for representative heterogeneous data center configurations, highlighting the significant potential of heterogeneity-aware management.

Combining compiler and operating system support for energy efficient i/o on embedded platforms

Mobile and embedded platforms have experienced dramatic advances in capabilities, largely due to the development of associated peripheral devices for storage and communication. The incorporation of these I/O devices has increased the overall power envelope of these platforms. In fact, system-level power consumption of mobile platforms is often dominated by peripheral devices. Since battery technologies alone have been unable to provide the lifetimes required by many platforms, in order to conserve energy, most devices provide the ability to transition into low power states during idle periods. The resulting energy savings are heavily dependent upon the lengths and number of idle periods experienced by a device. This paper presents an infrastructure designed to take advantage of device low power states by increasing the burstiness of device accesses and idle periods to provide a reduced power profile, and thereby an improvement in battery life. Our approach combines compiler-based source modifications with operating system support to implement a dynamic solution for enhanced energy consumption. We evaluate our infrastructure on an XScale-based embedded platform with a Linux implementation. 1