Fog-enabled Joint Computation, Communication and Caching Resource Sharing for Energy-Efficient IoT Data Stream Processing
ABSTARCT :
Fog/edge computing has been recently regarded as a promising approach for supporting emerging mission-critical Internet of Things (IoT) applications on capacity and battery constrained devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation, communication and caching resource sharing (i.e., 3C resources), it enables great potentials in low-latency and energy-efficient IoT task execution. To efficiently exploit 3C resources of fog devices in proximity, we propose F3C, a fog-enabled 3C resource sharing framework for energy-efficient IoT data stream processing by solving an energy cost minimization problem under 3C constraints. Nevertheless, the minimization problem proves to be NP-hard via reduction from a Generalized Assignment Problem (GAP). To cope with such challenge, we propose an efficient F3C algorithm based on an iterative task team formation mechanism which regards each task's 3C resource sharing as a subproblem solved by the elaborated min cost flow transformation. Via utility improving iterations, the proposed F3C algorithm is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our F3C algorithm can achieve superior performance in energy saving compared to various benchmarks.
EXISTING SYSTEM :
? Fog computing complements existing cloud architecture and has addressed the issue of latency and bandwidth efficiency.
? It supports all existing legacy devices and non-IIoT devices that never intended to be the part of IIoT application.
? Interface of Industry 4.0 with existing smart infrastructure such as smart buildings, smart homes, smart grids, smart logistics, social web, and business web build a CPS system.
? The integration of these heterogeneous devices in a network with other devices and existing communication technologies is also the main requirement for designers.
? Virtualization makes it easiest to deploy fog functionalities on an existing node (by isolating and securing fog services in a virtual machine or container).
DISADVANTAGE :
? To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency.
? However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed.
? They formulated the collaborative joint caching and processing problem as an optimization problem that aims to minimize the backhaul network cost, subject to cache capacity and processing capacity constraints.
? The problem of joint caching and communication for drone-enabled systems is also studied in.
PROPOSED SYSTEM :
• This proposed scheme has helped in finding the exact location of the source node in terms of energy efficiency and privacy.
• The proposed SDN-enabled fog computing architecture had three-layered structure namely, fog layer, network layer and application layer.
• They have proposed Computation-Offloading-Decision-Making and Resource-Allocation Algorithm (CORA) to minimize the maximal network cost (delay & energy consumption).
• A hybrid semi-distributed resource allocation algorithm was proposed by the authors for the proposed weighted sum-rate maximization problem.
• They have proposed three parallel algorithms to improve latency, throughput and resource management.
ADVANTAGE :
? In, order to significantly reduce redundant data transmissions and improve content delivery, the authors highlighted the need of having efficient content caching and distribution techniques.
? Therefore, when each MEC server operates independently, it cannot efficiently handle big data stemming from users’ devices and significantly relieve the data exchange between users’ devices and the remote cloud.
? However, due to the limited cache capacity, the MNO needs to evict from the cache the least frequently reused data in order to make room for new incoming data that needs to be cached.
? BSUM can be used for solving separable smooth or non-smooth convex optimization problems that have linear coupling constraints.
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