Hierarchical Scheduling Mechanisms in Multi-Level Fog Computing
ABSTARCT :
Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as cloudlets, or fog nodes. Therefore, this paper proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application's latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Our component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11% compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.
EXISTING SYSTEM :
? In existing cloud infrastructures, the data are sent to cloud servers for further processing and then returned to the devices.
? Collect research studies regarding the architectural overview for cloud, fog, and edge computing and research on existing resource management techniques.
? There is a certain amount of time that is needed to accomplish the communication between the cloud and the existing IoT devices, which will be automatically added to the processing time.
? In other words, the research work represents an analytical examination and discussion on existing studies about resource management.
DISADVANTAGE :
? This paper’s contributions focus on addressing these three issues for a multi-layered fog computing environment and latency sensitive applications.
? Due to the fog nodes’ limited computing capabilities to satisfy all the resource requests from the IoT applications, they addressed the problem using a competitive game approach to allocate resources efficiently.
? The deployment of cloud-like computing services closer to users introduces significant resource management issues that are still open.
? It negatively impacts the use of resources at the network edge, causing massive delays into the EEGTBG application loop, especially when EEGTBG faces a heavy workload.
PROPOSED SYSTEM :
• To manage a large set of tasks that are working together and are dependent on a certain set of resources, task scheduling algorithms have been proposed to define a schedule to service tasks to avoid conditions such as deadlocks.
• However, the study could be further extended by providing a proper detailed list of proposed solutions from the reviewed literature, and respectively their classification.
• In, a workload balancing algorithm is proposed for fog computing, aiming to reduce the data flow latency in the transmission procedures by connecting IoT devices to the appropriate base stations (BSs).
• The proposed methodology was tested with iFogSim and analyzed with different existing dynamic algorithms.
ADVANTAGE :
? Multi-level Fog computing is a complementary of the traditional architecture (which contains one cloudlet level) to enhance the performance services, reducing delay, energy consumption and network usage in IoT environment.
? Fog-aware mechanisms to implement efficient resource management for applications with different levels of latency requirements is indeed a challenging task.
? In this paper, however, we introduce an efficient scheduling algorithm for real-time applications, such as online games and video tracking surveillance, for multi-tiered cloudlet infrastructures.
? Our proposed CB-E allocation policy could be used by telco service providers/operators to mitigate this overload issue during this time.
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