| dc.description.abstract | The vision of the Internet of Things (loT) is made possible by advancements in sensor technologies, smart devices, wearable gadgets, and communication paradigms. IoT Service Providers (loSPs) provide loT services such as smart cities, virtual and augmented reality and pervasive healthcare. These services produce a significant amount of time-sensitive data to be processed. IoT devices can not process these data due to resource and energy constraints. Centralized cloud computing services can provide on-demand computationaland storage capabilities through the Internet. Users of cloud computing benefit from minimal startup costs for their IT needs, unlimited resources, and easy ac cess to them. Therefore, IoSPs send task requests to process these data to Cloud Service Providers (CSPs). However, the emerging trend of delay-sensitive appli cations demands low latency, privacy, and context awareness, which can barely be satisfied by applications processed at distant cloud centers as it takes a signif icant amount of time to send and receive a massive volume of data. Several newa distributed computing models have emerged that provide computing and storage services close to service consumers to satisfy the latency and context awareness needs of the applications such as Fog computing, Cloudlets and Micro data cen ters. They extend the cloud services to the network edge and give services to the end-users with low latency and decrease data traffic.Fog Service Providers (FSPs) are available in the market who have deployed computation servers that can act as fog devices. CSPs can offload the task request received from IoSP to FSP to decrease the service delay and pay to FSP from the payment received from IoSPs. The IoT tasks have Quality of Service (QoS) re quirements such as deadlines and priorities. In addition, IoT tasks are online in nature. As a result, task requirements are not known in advance. The allocation of resources to these tasks in a commercial three-tier architecture is a complex prob lem since QoS requirements of IoT applications and competition among loSPs and FSPs for the price of the resources must be considered. The perishable nature of cloud-fog resources makes allocation more challenging. Resources not allocated at a specific time cannot be reused later. Considering this property when deter mining the price of resources and allocations is essential.In such a competitive market where each service provider, such as loSPs, CSPs, and FSPs, wishes to maximize its profit, auction mechanisms are the best tools for finding prices of resources and allocating them in a manner that allows service providers to benefit from market demand and IoSPs to benefit from competition among providers. In our model, CSP acts as a broker between IoSPs and FSPs. FSPs have resources to sell with price demand, and IoSPs have task requirements with the cost they are willing to pay. FSPs want to sell at a maximum price, and IoSPs want to purchase resources at a minimum price. Moreover, due to the per ishable nature of the resources, if FSPs bid too high, resources will not be allo cated, resulting in their loss. The double auction can be used to find equilibrium in this situation. A multi-attribute double auction mechanism is employed in oura model to account for both QoS requirements of tasks and monetary competition between IoSPs and FSPs.We perform task allocation in batch mode and online mode. Batch mode al location is done at every fixed interval. It is assumed that task requests can be generated at any time. Depending on the criticality of the task, it can be executed in batch mode or online mode. In batch mode, eligible FSP is found for eacha task, and then (TASC) is used for price determination. After that, considering the perishable nature of the resources, the remaining resources are also allocated at a lower price with some constraints on IoSPs. For doing the auction for online tasks, there are no other competitors to compare and make efficient decisions. We perform a virtual auction at the beginningof the system to handle this situation, giving us the critical price for differentsupply-demand scenarios and QoS levels. This critical price decides whether an online task request should be accepted or rejected. Also, the dynamic nature of the Internet, the scarcity of resources in the cloud fog, and the variability in service rates in the cloud fog may delay the execution of IoT tasks after allocation, low ering the task value. Therefore, service delay between IoSP and allocated FSP is monitored after allocating the tasks. If, for any reason, it is observed that the task may not be completed before the deadline, then more resources are allocated, or the task is migrated to another FSP. We prove that our algorithm is a polynomial time algorithm of time complexity O(N2K2M) where N is the number of IoSPs, a M is the number of FSPs, and K is the number of tasks.The remote patient monitoring system is used as a case study to verify the pro posed QoS-aware Task Allocation and Scheduling (QoTAS). We consider the tasks performed in remote patient monitoring systems with their resource and QoS re quirements. The Internet behavior is simulated in Netsim to get network delay between FSP-IoSP and FSP-FSP. We perform experiments to compare the batch allocation algorithm (QoTAS-B) with Multi attribute-based double auction mech anism (MADA) [81] and batch with online allocation algorithm (QoTAS-BO) with Reverse auction-based online allocation (RAOA) [18]. Which shows proposed algorithm outperforms the existing work [81] and [18]. Also, results show that migration increases the task completion ratio. | |
