Page 23 - Big Data Analytics for Intelligent Healthcare Management

1.4 FUTURE RESEARCH DIRECTIONS AND OPEN CHALLENGES 13

need to optimize provisioning of cloud resources in existing bio-inspired algorithms for big data an-
alytics. To solve this challenge, a quality of service (QoS)-aware bio-inspired algorithm-based resource
management approach is required for the efficient management of big data to optimize the QoS
parameters.

1.4.2 DATA PROCESSING AND ELASTICITY
There is a challenge of data synchronization in bio-inspired algorithms due to data processing that is
taking place geographically, which increases overprovisioning and underprovisioning of cloud re-
sources. There is a need to identify the overloaded resources using rapid elasticity, which can handle
the data received from different IoT devices. To improve the recoverability of data, there is a need for a
data backup technique for big data analytics, which can provide the service during server downtime.

1.4.3 RESILIENCE AND HETEROGENEITY IN INTERCONNECTED CLOUDS
The cloud providers such as Microsoft, Amazon, Facebook, and Google are delivering reliable and
efficient cloud service by utilizing various cloud resources such as disk drives, storage devices, net-
work cards, and processors for big data analytics. The complexity of computing systems is increasing
with an increasing size of cloud data centers (CDCs), which increases the resource failures during big
data analytics. The resource failure can be premature termination of execution, data corruption, and
service level agreement (SLA) violation. There is a need to find out more information about the failures
to make the system more reliable. There is a need for replication of cloud services to analyze the big
data in an efficient and reliable manner.

1.4.4 SUSTAINABILITY AND ENERGY-EFFICIENCY
To reduce energy consumption, there is a need to migrate user data to more reliable servers for efficient
execution of cloud resources. Moreover, introducing the concept of resource consolidation can increase
the sustainability and energy efficiency of a cloud service by consolidating the multiple independent
instances of IoT applications.

1.4.5 DATA SECURITY AND PRIVACY PROTECTION
To improve the reliability of distributed cloud services, there is a need to integrate security protocols in
the process of big data analytics. Further, there is a need to incorporate authentication modules at dif-
ferent levels of data management.

1.4.6 IoT-BASED EDGE COMPUTING AND NETWORKING
There are a large number of edge devices participating in the IoT-based Fog environment to improve
the computation and reduce the latency and response time, which can further increase the energy con-
sumption. Fog devices are not able to offer resource capacity in spite of additional computation and
storage power. There is a need to process the user data at an edge device instead of at the server, which
can reduce execution time and cost.