59 research outputs found
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
Non-Invasive Induction Link Model for Implantable Biomedical Microsystems: Pacemaker to Monitor Arrhythmic Patients in Body Area Networks
In this paper, a non-invasive inductive link model for an Implantable
Biomedical Microsystems (IBMs) such as, a pacemaker to monitor Arrhythmic
Patients (APs) in Body Area Networks (BANs) is proposed. The model acts as a
driving source to keep the batteries charged, inside a device called,
pacemaker. The device monitors any drift from natural human heart beats, a
condition of arrythmia and also in turn, produces electrical pulses that create
forced rhythms that, matches with the original normal heart rhythms. It
constantly sends a medical report to the health center to keep the medical
personnel aware of the patient's conditions and let them handle any critical
condition, before it actually happens. Two equivalent models are compared by
carrying the simulations, based on the parameters of voltage gain and link
efficiency. Results depict that the series tuned primary and parallel tuned
secondary circuit achieves the best results for both the parameters, keeping in
view the constraint of coupling co-efficient (k), which should be less than a
value \emph{0.45} as, desirable for the safety of body tissues.Comment: IEEE 8th International Conference on Broadband and Wireless
Computing, Communication and Applications (BWCCA'13), Compiegne, Franc
A study on incremental mining of frequent patterns
Data generated from both the offline and online sources are incremental in nature. Changes in the underlying database occur due to the incremental data. Mining frequent patterns are costly in changing databases, since it requires scanning the database from the start. Thus, mining of growing databases has been a great concern. To mine the growing databases, a new Data Mining technique called Incremental Mining has emerged. The Incremental Mining uses previous mining result to get the desired knowledge by reducing mining costs in terms of time and space. This state of the art paper focuses on Incremental Mining approaches and identifies suitable approaches which are the need of real world problem.Keywords: Data Mining, Frequent Pattern, Incremental Mining, Frequent Pattern Minung, High Utility Mining, Constraint Mining
An Analytical Model-based Capacity Planning Approach for Building CSD-based Storage Systems
The data movement in large-scale computing facilities (from compute nodes to
data nodes) is categorized as one of the major contributors to high cost and
energy utilization. To tackle it, in-storage processing (ISP) within storage
devices, such as Solid-State Drives (SSDs), has been explored actively. The
introduction of computational storage drives (CSDs) enabled ISP within the same
form factor as regular SSDs and made it easy to replace SSDs within traditional
compute nodes. With CSDs, host systems can offload various operations such as
search, filter, and count. However, commercialized CSDs have different hardware
resources and performance characteristics. Thus, it requires careful
consideration of hardware, performance, and workload characteristics for
building a CSD-based storage system within a compute node. Therefore, storage
architects are hesitant to build a storage system based on CSDs as there are no
tools to determine the benefits of CSD-based compute nodes to meet the
performance requirements compared to traditional nodes based on SSDs. In this
work, we proposed an analytical model-based storage capacity planner called
CSDPlan for system architects to build performance-effective CSD-based compute
nodes. Our model takes into account the performance characteristics of the host
system, targeted workloads, and hardware and performance characteristics of
CSDs to be deployed and provides optimal configuration based on the number of
CSDs for a compute node. Furthermore, CSDPlan estimates and reduces the total
cost of ownership (TCO) for building a CSD-based compute node. To evaluate the
efficacy of CSDPlan, we selected two commercially available CSDs and 4
representative big data analysis workloads
WLAN aware cognitive medium access control protocol for IoT applications
© 2020 by the authors. Internet of Things (IoT)-based devices consist of wireless sensor nodes that are battery-powered; thus, energy efficiency is a major issue. IEEE 802.15.4-compliant IoT devices operate in the unlicensed Industrial, Scientific, and Medical (ISM) band of 2.4 GHz and are subject to interference caused by high-powered IEEE 802.11-compliant Wireless Local Area Network (WLAN) users. This interference causes frequent packet drop and energy loss for IoT users. In this work, we propose a WLAN Aware Cognitive Medium Access Control (WAC-MAC) protocol for IoT users that uses techniques, such as energy detection based sensing, adaptive wake-up scheduling, and adaptive backoff, to reduce interference with the WSN and improve network lifetime of the IoT users. Results show that the proposedWAC-MAC achieves a higher packet reception rate and reduces the energy consumption of IoT nodes
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