ABSTARCT :
The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this work, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal (i.e., utility-maximizing) resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. drawn from the economics literature. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms are introduced, which efficiently converge to an ME. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.
EXISTING SYSTEM :
Different from the existing works on cloud economics and resource allocation in general, our design objective is to find a fair and efficient way to allocate resources from multiple nodes (e.g., ENs) to budget-constrained agents (i.e., services), which makes every agent happy with her resource allotment and ensures high edge resource utilization. The proposed model also captures practical aspects, for example, a service request can be served at different ENs and service demands can be defined flexibly rather than fixed bundles as in auction models.
PROPOSED SYSTEM :
We furthermore link the ME to the fair division literature [7] and prove that the allocation satisfies remarkable fairness properties including envy-freeness, sharing-incentive, and proportionality, which provides strong incentives for the services to participate in the proposed scheme. Indeed, these properties were rarely investigated explicitly in the ME literature. Envy-freeness means that every service prefers its allocation to the allocation of any other service. In an envy-free allocation, every service feels that its share is at least as good as the share of any other service, and thus no service feels envy. Sharing-incentive is another well-known fairness concept. It ensures that services get better utilities than what they would get in the proportional sharing scheme that gives each service an amount of resource from every EN proportional to its budget. Note that proportional sharing is an intuitive way to share resources fairly in terms of quantity. For the federation setting, sharing-incentive implies that every service gets better off by pooling their resources (or money) together. Finally, it is natural for a service to expect to obtain a utility of at least b/B of the maximum utility that it can achieve by getting all the resources, where b is the payment of the service and B is the total payment of all the services. The proportionality property guarantees that the utility of every service at the ME is at least proportional to its payment/budget. Thus, it makes every service feel fair in terms of the achieved utility.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
