Scheduling of task is one of the most important aspects in cloud computing, edge computing, and mobile cloud computing. It becomes very prominent issue when a framework and its scheduling algorithm caters to task scheduling at the local cloud and remote cloud with multiple cloud environment. This research paper mainly focuses on scheduling of independent and workflow-based tasks at local cloud and remote cloud. It proposes an advanced scheduling mechanism enabled-scalable key parameter yield of resources framework which is an improved version of SKYR framework which has SKYR framework-based task allocation to resources (STAR) algorithm which manages the various aspects of task scheduling at both local and remote cloud. This framework along with its algorithm, manages heterogenous edge cloud at local level and also caters the multiple cloud environment at remote cloud which is the unique feature of the proposed work. This proposed STAR algorithm uses some prominent and established methods such as laxity-based priority access, constraint optimization algorithm based on ant colony system, execution time computation matrix, genome interpretation and crossover scheduling for efficient and cost-effective scheduling. Moreover, it also facilitates reliability and scalability in computation at both levels and also allows comprehensive interaction among various entities involved to provide effective computation to the multiple types of users.