Abstract
Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.
Highlights
With fast-growing advancements in smart systems, the real-time applications are handy candidates for utilizing the computing power in a cloud computing environment in order to maintain deadline constraints
Infrastructure-as-a-Service (IaaS) that deals with providing computing such as virtual machines (VMs) and storage resources as services, Platform-as-a-Service (PaaS) that offer deployment and development platforms as services, and Software-as-a-Service (SaaS) that facilitate users with web-based applications
We assume that the data storage resource gives a response immediately when a request is made by a task for data file access and the response time is ignored
Summary
With fast-growing advancements in smart systems, the real-time applications are handy candidates for utilizing the computing power in a cloud computing environment in order to maintain deadline constraints. The cloud storage resources provide facilities such as accommodating data replication to satisfy data requirements of the data-intensive real-time systems that need to access, process and transfer data files stored in distributed data repositories [1,2]. Examples of such applications are self-driving vehicles, which depend on the data and computations under a complex network of interconnected devices such as GPS, surveillance cameras, radar, laser light, odometry, etc., to perceive the surroundings [3,4]. There is a lack of efficient resource scheduling and allocation strategies for deploying real-time applications with stringent QoS and data requirements in cloud computing environments
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