SDFog

Summary:

This paper proposes a service-oriented middleware “SDFog” that distributes service hosting throughout the fog environment. All the participating nodes - from the edge to the cloud - capable of hosting services. The system provides an interface to applications for submitting service orchestration tasks which can be bound by QoS constraints. The system performs QoS aware deployment by scheduling flows between services that satisfy specified QoS constraints.

Virtual network functions (VNFs) are used for establishing healthy user experience, which are allowed to run on the same infrastructure used by user applications and access resources of the underlying devices through a hypervisor. Distributed service discovery is performed, followed by QoS aware flow creation and installation by the SDFog controller.This article extends the concept of SDN to the application layer, and into a ”Software Defined Fog” (SDFog), which is able to execute dataplane functions dealing with compute, storage and network resources on fog nodes.

It describes an exemplary use-case using a prototype framework “HSH” where the user experience can be improved by installing VNFs at appropriate points in the network.

Strengths:

1) The paper extended concept of SDN to application layer, and into the “Software Defined Fog”.

2) The service metadata for services hosted on a fog node allows for service selection under constraints imposed by applications.

3) Applications have an advantage of specifying QoE parameters during their execution.

4) Because to highly unpredictable physical network bandwidth, flow creation builds an overlay network of orchestrated services over the physical network topology in a QoS-satisfying fashion.

Network bottlenecks such as jitters, delays, and packet loss can be mitigated either by shaping traffic or changing the forwarding path.  

5) Network function virtualization allows flexible placement of network functionality at different points in the network topology, which can be leveraged to deploy QoE enhancing functions like traffic shapers or WAN accelerators at points where congestion can deteriorate user experience

6) A great illustration of the paradigm using the prototype Health Smart Home (HSH).  

7) A good portion of this research is built on the top of well researched areas such as SDN, VNF and fog computing.

Weaknesses/Discussion points:

1)  There should be robust mechanisms for service orchestration, which should ensure consistency across services.Multiple applications might send conflicting actions to the same actuator service. The actuator service should have a way to figure out the best action which doesn’t heavily impact the application performance.  

2) Currently, the authors have only explored the networking side of SDN, that is they are only utilizing SDN networking API using NFV. Considering resources such as compute and storage will help in extending this paradigm to a framework which controls fog infrastructure.

3) Since the environment is dynamic and heterogenous, there should be robust  service discovery, deployment and dynamic reconfiguration mechanism in place.  

4) Applications should be able to specify QoS parameters for at an abstract level, which should then be translated into low-level network decisions by the SDFog controller.

Dynamic Resource Provisioning Through Fog Micro Datacenter

Summary:

In present scenario many heterogeneous devices constitute IoT. It highly unpredictable that how much resources would be consumed and whether the requesting node, device, or sensor is going to fully utilize the resources it has requested. Due to this uncertainty, the authors have incorporated Relinquish probability (the probability of a user releasing the resource) while performing resource estimation in their model. The proposed model presents user characteristic based resource management for Fog, taking into account the type of service, overall service relinquish probability, and service oriented relinquish probability. They have also considered variance in relinquish probability to know the exact deviation and irregularity factor in give-up probability. This methodology helps determine the right amount of resources required, avoiding resource wastage and profit-cut for the CSP as well as the Fog itself.

Strengths:

1) This study is the first of its kind to explore resource management in fog. Previous studies mostly focus on resource management in cloud.  

2) This framework not just does resource allocation, it also predicts resource utilization based on user’s profile which includes his/her past usage trends and probability of using those resources in future.

3) Variance of service oriented relinquish probabilities has been considered to account for fluctuations in resource utilization by CSCs. This provides better results as it determines the actual behavior of each customer.

4) The amount of resource allocated to a CSC also depends on the type of service. For each customer a Virtual resource value (VRV) is calculated. This value is then mapped to actual resources.

In the scenario when a CSC has already been a customer of CSP before, but requested a particular service 𝑆 for the first time, resources are estimated differently. In this case, Fog allocates resources keeping in view the available record, but assuming that the CSC is going to be somewhat loyal in utilizing current service 𝑆. Main idea is to incorporate available historical data as much as possible, so that the CSC is dealt accordingly, with fairness and CSP and Fog have minimum possible risk.  

Weaknesses/Discussion Points:

1) An enhancement that can be done to this framework is that resource allocation should be done based on other parameters such as whether the CSC is a premium customer or not. In that case more complicated measures should be put into place instead of just considering the past usage.

2) There should also be consideration for moving resources across resource pools based on the usage in each resource pool. This is a sort of load balancing within resource pools.

3) For CSCs, there should be an option to ask whether they would require the amount of resources as predicted by CSP or if they have some special needs for this particular request. This would help in predetermining any aberrations in the usage pattern of a user and will eventually lead to a better user experience.

4) The overall performance of this framework will deteriorate if the usage patterns of CSCs are highly fluctuating, as resources have to be requested and relinquished with high frequency causing overhead.