Resource Provisioning and Allocation in Function-as-a-Service Edge-Clouds

Abstract : Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited capacity. In this paper, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions that process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.

 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. ? The proposed methodology was tested with iFogSim and analyzed with different existing dynamic algorithms.

 DISADVANTAGE :

 ? In this paper, we address the problem of energyaware optimization of capacity provisioning and resource allocation in edge computing systems. ? We formulate the problem as a mixed integer linear program and prove that the problem is NP-hard. ? However, their proposed algorithm is based on solving a mixed-integer linear program which might not be feasible to solve within a reasonable amount of time for large size problems. ? In this paper, we address the capacity provisioning and resource allocation problem in EC systems with the aim of maximizing the net profit of the provider while taking into account the energy consumption of the system.

 PROPOSED SYSTEM :

 • To accommodate some of these features, the computational paradigm fog computing was proposed. • Their results represent a mapping between the proposed taxonomy and existing literature on the cloud, fog, and edge computing paradigm. • However, the study could be further extended by providing a proper detailed list of proposed solutions from the reviewed literature, and respectively their classification. • 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.

 ADVANTAGE :

 ? We evaluate the performance of the proposed algorithm by conducting an extensive experimental analysis on problem instances of various sizes. ? We present an experimental analysis of the performance of our proposed algorithm, G-ECPRA. We compare the performance of G-ECPRA with the optimal solution obtained by solving ECPRA-MILP with CPLEX . ? We first investigate the effects of the number of users on the performance of GECPRA. ? This indicates that our algorithm has a stable behavior and that the value of P CR does not significantly affect the performance of the algorithm. ? In order to solve the problem efficiently, we proposed a heuristic algorithm and analyzed its performance.