Explore the vast range of titles available.

An accurate host power model is necessary for effective power management in data centers which is crucial for reducing energy consumption and cost. One should evaluate the power models for different workloads and host configurations. We have analyzed several existing power models by varying the workload type (CPU, memory, and disk-intensive) and host configurations. By analyzing the system performance and nature of the power consumption of the hosts, we have identified some performance counter parameters that determine the system power consumption. We have proposed three power models based on multi-variable Linear Regression, Support Vector Regression (SVR), and Artificial Neural Network (ANN). Experimental results show that compared to the existing models, our proposed power models, especially those based on SVR and ANN, more accurately predict the power consumption of the hosts. We have also conducted simulation experiments to show the importance of the power model in the energy-efficient resource management of the hosts in the data center. Results show that the use of our SVR-based and ANN-based power models in a resource management approach can effectively decrease the energy consumption of the data center. Moreover, we have proposed an energy-efficient virtual machine (VM) placement and consolidation algorithm that further reduces energy consumption. At first, we formulated a model using integer linear programming. Then, we designed a heuristic based on Vogel’s Approximation Method. Extensive simulation on the CloudSim platform with benchmark workload data and the Google Cloud trace logs shows that our approach outperforms the state-of-the-art algorithms under comparison in terms of energy efficiency and quality of service (QoS). The results also highlight the importance of a suitable VM placement and consolidation approach and an accurate power model in reducing energy consumption.

Workflows are special applications used to solve complex scientific problems. The emerging Workflow as a Service (WaaS) model provides scientists with an effective way of deploying their workflow applications in Cloud environments. The WaaS model can execute multiple workflows in a multi-tenant Cloud environment. Scheduling the tasks of the workflows in the WaaS model has several challenges. The scheduling approach must properly utilize the underlying Cloud resources and satisfy the users’ Quality of Service (QoS) requirements for all the workflows. In this work, we have proposed a heurisine-sensitive workflows in a containerized Cloud environment for the WaaS model. We formulated the problem of minimizing the MIPS (million instructions per second) requirement of tasks while satisfying the deadline of the workflows as a non-linear optimization problem and applied the Lagranges multiplier method to solve it. It allows us to configure/scale the containers’ resources and reduce costs. We also ensure maximum utilization of VM’s resources while allocating containers to VMs. Furthermore, we have proposed an approach to effectively scale containers and VMs to improve the schedulability of the workflows at runtime to deal with the dynamic arrival of the workflows. Extensive experiments and comparisons with other state-of-the-art works show that the proposed approach can significantly improve resource utilization, prevent deadline violation, and reduce the cost of renting Cloud resources for the WaaS model.

Cloud computing enables the execution of various applications submitted by the users in the virtualized Cloud environment. However, the Cloud infrastructure consumes a significant amount of electrical energy to provide services to its users that have a detrimental effect on the environment. Many of these applications (tasks), like those belonging to the healthcare system, scientific research, the Internet of Things (IoT), and others, are deadline-sensitive. Hence efficient scheduling of tasks is essential to prevent deadline violation, decrease makespan, and at the same time reduce energy consumption. To address this issue, we have considered the bi-objective optimization problem of minimization of energy and makespan and have proposed two scheduling approaches for independent, deadline-sensitive tasks in a heterogeneous Cloud environment. Our first approach is a greedy heuristic based on the Linear Weighted Sum technique. The second one is based on Ant Colony Optimization and uses a combination of heuristic search and positive feedback of information to improve the solution. Both approaches use a three-tier model where tasks are scheduled by taking into account the properties of three entities- tasks, VMs, and hosts. Moreover, we have proposed a suitable strategy for scaling of Cloud resources to improve energy-efficiency and task schedulability. Extensive simulations using Google Cloud trace-logs and comparison with some state-of-art approaches validate the effectiveness of our proposed scheduling techniques in achieving a proper trade-off between the energy consumption of the virtualized Cloud infrastructure and the average makespan of the tasks.

Convergecast is the process of gathering all sensor data at the base station. Convergecast in a Sensor-Cloud is achieved by creating multiple data-gathering trees over collaborating wireless sensor networks and collecting sensor data via those trees. Designing a schedule for convergecast can be one-shot (only for one round of data collection) or pipelined (data collected at regular intervals repeatedly). We give a pipelined algorithm for convergecast, assuming a raw data model (no aggregation). Firstly, we design a slot allocation algorithm considering only the adjacency constraints and traversing the trees in BFS (breadth first search) order. Our schedule requires at most M+H+1 time slots for the first round of data collection and at most M+1 slots in subsequent rounds, where M is the lower bound to complete one round of convergecast and H is the maximum height of the data gathering trees. Our schedule does not require any node to store more than two packets. In the next step, we consider interference among simultaneous transmissions and allocate multiple frequency channels to ensure interference below a certain threshold. The problem of minimizing the frequency channels while maintaining SINR above the desired value is reduced to a semidefinite programming relaxation. In our simulation, the time for the first round of convergecast is much lower than M+H+1 (closer to M+1), and the channel allocation algorithm requires either optimal or just one more than the optimal number of channels.

Mobile crowd sensing (MCS) is an emerging sensing platform that concedes mobile users to efficiently collect data and share information with the MCS service providers. Despite its benefits, a key challenge in MCS is how beneficially select a minimum subset of participants from the large user pool to achieve the desired level of coverage. In this paper, we propose several algorithms to choose a minimum number of mobile users(or participants) who met the desired level of coverage. We consider two different cases, in the first case, only a single participant is allowed to upload a data packet for a particular target, whereas for the other case, two participants are allowed to do the same (provided that the target is covered by more than one participants). An optimal solution to the problem can be found by solving integer linear programmings (ILP’s). However, due to the exponential complexity of the ILP problem, for the large input size, it is infeasible from the point of execution time as well as the requirement of having the necessary information about all the participants in a central location. We also propose a distributed participant selection algorithm considering both the cases, which are dynamic in nature and run at every user. Each user exchanges their message with the neighbors to decide whether to remain idle or active. A series of experiments are executed to measure the performance of the proposed algorithms. Simulation results reveal the proximity of the proposed distributed algorithm compared to the optimal result providing the same coverage.

A crucial research problem in the field of wireless sensor network is to maximize its lifetime. One approach to solve this problem is to group the nodes in clusters or chains. One of the nodes in a cluster or chain takes the responsibility of collecting data from the other sensors in its group and sends the data to the base station or sink. As the energy spent per round is comparatively higher for a cluster head(CH)/chain leader (CL), the lifetime of the network can increase if we rotate the role of CH/CL among the nodes in the cluster/chain. In this paper, we have proposed several algorithms to select CH/CL at different rounds to maximize the lifetime of the WSN. We have shown that maximizing lifetime measured as first node die, can be solved optimally using an integer linear program. Then we have given a polynomial-time algorithm to solve this problem by relaxing to a linear programming problem. We have also given algorithms to maximize the lifetimes measured as half of the nodes die and last node dies. Simulation results indicate significant improvement in the performance of our proposed algorithms compared to BP-DCA, NEECP and naive Bayesian-based algorithm.

In this paper we have classified the nodes of OTIS-cube based on their eccentricities. OTIS (optical transpose interconnection system) is a large scale optoelectronic computer architecture, proposed in \\cite{KMKE92}, that benefit from both optical and electronic technologies. We show that radius and diameter of OTIS-\\(Q_n\\) is \\(n+1\\) and \\(2n+1\\) respectively. We also show that average eccentricity of OTIS-cube is \\((3n/2+1)\\). In \\cite{D05}, a variant of OTIS-cube, called Enhanced OTIS-cube (E-OTIS-\\(Q_n\\)) was proposed. E-OTIS-\\(Q_n\\) is regular of degree \\(n+1\\) and maximally fault-tolerant. In this paper we have given a classification of the nodes of E-OTIS cube and derived expressions for the eccentricities of the nodes in each class. Based on these results we show that radius and diameter of E-OTIS-\\(Q_n\\) is \\(n+1\\) and \\(\\lfloor {4n+4/3} \\rfloor\\) respectively. We have also computed the average eccentricity of E-OTIS-\\(Q_n\\) for values of \\(n\\) upto 20.

Cloud Service Providers (CSPs) adapt different pricing models for their offered services. Some of the models are suitable for short term requirement while others may be suitable for the Cloud Service User's (CSU) long term requirement. In this paper, we look at the problem of finding the amount of resources to be reserved to satisfy the CSU's long term demands with the aim of minimizing the total cost. Finding the optimal resource requirement to satisfy the the CSU's demand for resources needs sufficient research effort. Various algorithms were discussed in the last couple of years for finding the optimal resource requirement but most of them are based on IPP which is NP in nature. In this paper, we derive some heuristic-based polynomial time algorithms to find some near optimal solution to the problem. We show that the cost for CSU using our approach is comparable to the solution obtained using optimal Integer Programming Problem(IPP).

Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions.

Lack of surveillance systems and accurate data impede evidence-based decisions on treatment and prevention of enteric fever, caused by Salmonella Typhi/Paratyphi. The WHO coordinates a global Invasive Bacterial-Vaccine Preventable Diseases (IB-VPD) surveillance network but does not monitor enteric fever. We evaluated the feasibility and sustainability of integrating enteric fever surveillance into the ongoing IB-VPD platform. The IB-VPD surveillance system uses WHO definitions to enroll 2-59 month children hospitalized with possible pneumonia, sepsis or meningitis. We expanded this surveillance system to additionally capture suspect enteric fever cases during 2012-2016, in two WHO sentinel hospitals of Bangladesh, by adding inclusion criteria of fever ≥102°F for ≥3 days, irrespective of other manifestations. Culture-positive enteric fever cases from in-patient departments (IPD) detected in the hospital laboratories but missed by the expanded surveillance, were also enrolled to assess completion. Costs for this integration were calculated for the additional personnel and resources required. In the IB-VPD surveillance, 5,185 cases were enrolled; 3% (N = 171/5185) were positive for microbiological growth, of which 55% (94/171) were culture-confirmed cases of enteric fever (85 Typhi and 9 Paratyphi A). The added inclusion criteria for enteric fever enrolled an additional 1,699 cases; 22% (358/1699) were positive, of which 85% (349/358) were enteric fever cases (305 Typhi and 44 Paratyphi A). Laboratory surveillance of in-patients of all ages enrolled 311 additional enteric fever cases (263 Typhi and 48 Paratyphi A); 9% (28/311) were 2-59 m and 91% (283/311) >59 m. Altogether, 754 (94+349+311) culture-confirmed enteric fever cases were found, of which 471 were 2-59 m. Of these 471 cases, 94% (443/471) were identified through the hospital surveillances and 6% (28/471) through laboratory results. Twenty-three percent (170/754) of all cases were children <2 years. Additional cost for the integration was USD 44,974/year, a 27% increase to the IB-VPD annual expenditure. In a setting where enteric disease is a substantial public health problem, we could integrate enteric fever surveillance into the standard IB-VPD surveillance platform at a modest cost.