How to improve resource utilization of cloud data centers (CDCs) and ensure users' quality
of service (QoS) through efficient virtual machine (VM) scheduling is an urgent problem …

Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common
practice as cloud service providers (CSP) offers relevant applications and scalable …

Big Data applications are present in many areas such as financial markets, search engines,
stream services, health care, social networks, and so on. Data analysis provides value to …

Big data refers to diverse large data types from heterogeneous sources such as mobile
devices, the web, social media, and the internet of things. The cloud offers a wide variety of …

Improving the performance of MapReduce scheduler is a primary objective, especially in a
heterogeneous virtualized cloud environment. A map task is typically assigned with an input …

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range
of applications, including smart industry, smart healthcare, and smart cities. Providing on …

Big data overwhelmed industries and research sectors. Reliable decision making is always
a challenging task, which requires cost-effective big data processing tools. Hadoop …

MapReduce is a widely used programming model for large-scale data processing. However,
it still suffers from the skew problem, which refers to the case in which load is imbalanced …

Objective: Big Data processing is a demanding task, and several big data processing
frameworks have emerged in recent decades. The performance of these frameworks is …

In the era of big data, data storage and computation have become a mainstream issue.
Hadooop, as a distributed computing framework capable of handling large-scale datasets …