The challenges of Big data enabled caching over wireless networks as I read it from some paper is the Caching Capacity also transmitting the messages at several caches to a destination over a noisy network

The provision of big data over wireless networks enables high traffic exchange between gateway elements such as service providers, third parties, and end users. The requirements of this platform are huge data volume processing in less time; cleansing, parsing and formatting data; data analysis; statistical analysis and visualizations; and more importantly new cashing methods to support speed transmission.

Stakeholders of providing big data over wireless networks and their roles:

- The providers of Internet content are champions of trust from the user community. Caching deeper than Content Delivery Networks (CDNs). Apart from the security keys, they also hold extensive expertise in implementing caching techniques in the core networks. Content provider-only solutions cannot unleash the full potential of wireless caching, since they are limited to alienated boxes in the operator network that can perform caching only with legacy CDN techniques.

- The operators of telecommunication networks are well placed for wireless caching. Due to the particularities of coded caching and multi-access caching, the operators are in a unique position to implement new protocols in base stations, affect the standards for new mobile devices, and develop big data processing infrastructure that can realize wireless caching taking into account that wireless caching lies both at network and physical layers.

The efficient operation of such caches is very challenging and there are many challenges:

- Mitigating wireless link overload is one of the most challenging issues that require going beyond caching at the base stations and the mobile users.

- Caches used in wireless networks are typically small as compared to CDN caches, and one cache analysis is not sufficient.

- The popularity profile of traffic is highly unpredictable when non-aggregated.

- Another challenging issue is security; a common anti-caching argument relates to the operation of caching in a secure environment.

- In regard of cost, although the cost of a small cache is dwarfed by the base station cost, the total amount of installed memory in a mobile network can be considerable, therefore deciding to install wireless caching requires a careful cost analysis.

- To compute the optimal size of memory to install at each location, one needs to know the cost coefficients, the skewness of content popularity, and the local traffic distribution in cells.

Predicting the correlation of the data content, popularity,  and  satisfying  user’s quality-of-experience (QoE) in a noisy channel environment and supporting vertical handover are some of the challenges.

the main challenges is the cache capacity and how to predict the most popular request.

as we know that in big data there are a huge o data , in this day and in the future , so there will be a lot of data that are seem to be cached, but at the same time we must keep in our mind the cache capacity.

also it a challenge to predicate the most popular request ,when we predicate the request in advanced and pre-cache it , we will reduce the congested traffic.

Here are some of the main challenges regarding wireless caching and big Data:

        1.    Caching in a secure environment.

        2.    Doing a careful cost analysis:

How to decide where to install wireless caching must be planned carefully. (Memory Is Cheap But Not Free especially with big data).

       3.    Doing a Sufficient Cache Analysis:

At each cache, even if the popularities for the contents are known, How to find what contents to be stored is considered to be a difficult problem.

Hello Dr. Muneer..

From my point of view, nowadays, the growth of data is the major challenge in wireless networks infrastructures. That's mean managing and control of this big data-driven networks in cloud environments is pressing issue. The most important thing is to improve the system performance to confront latency, hit ratio, memory allocations, security, energy efficiency, cost efficiency, and so on should be further devised.

The main challenges of Big data enabled caching over wireless networks as i notify:

1-controlling or reducing the network traffic to reduce congestion, latency and packet loss.

2-security and privacy of caches

3-flow control and how control error challenges

Due to the growth of data, the main challenges related to Big data enabled caching over wireless networks as I read:

1. The Caching Capacity as controlling of which data to be stored in each cache to reduce congestion

2. The operation of Caching in a Secure environment

3. The energy of Caching will be less efficient

4. The cost efficiency will be reduced

Hello Dr. Muneer.

As I read in papers, the main problem is the capacity of cache, To offload the capacity-limited backhaul, small base stations  are equipped with finite storage capacity and cache a subset of contents from the library F. However, due to the sheer volume of contents and users, it is very challenging to process and extract useful information to cache users’ all contents at base stations , mainly due to limited

storage constraints and lack of sufficient backhauls.

Ref:

Zeydan, E., Baştuğ, E., Bennis, M., Kader, M. A., Karatepe, A., Er, A. S., & Debbah, M. (2016). Big data caching for networking: Moving from cloud to edge. arXiv preprint arXiv:1606.01581.

The main challenge is to foresee what will be a popular demand, where and when it will come. The could involve analyzing data from anywhere such as peoples, newspapers, websites and social networks. The analysis also needs to marks around a specific radio access node what kind of content search for and what is the consumer’s profile of these people.

the challenges of wireless caching include: how to improve the performance of the cache, how to enable cache collaboration, increase memory availability, protocol availability and security challenges.

The demand for mobile data is continuously increasing,which results from the prevalence of smart devices, and the desire for high-quality and low-latency services.It is the main reason for the big data and the result of these labor-intensive and expensive to establish new backhaul infrastructures and unrealistic to lay high-capacity fibers fore very small cell,low latency, hit ratio, memory allocations, security, energy efficiencyand cost efficiency.

As I read in papers ,main challenge is able to predict which partition of data is requested in futuer and for improvment performance of big data application we sholud consider data locality and cache locality .