Allocation-aware Task Scheduling for Heterogeneous Multi-cloud Systems

Abstract

Cloud computing is one of the growing technology usage for the day-to-day business operations in today's IT industry. The diverse features of cloud such as on-demand self-service, quality of service, pay-per-usage pricing, virtualization and elasticity make the cloud more popular in industries as well as research communities. However, the mapping of the cloud resources in forms of virtual machines (VMs) to fulfill the customer requests is very challenging and a well-known NP-Complete problem. In this paper, we propose an allocation-aware task scheduling (ATS) algorithm for heterogeneous multi-cloud systems. The algorithm has three phases, namely matching, allocating and scheduling that aim to map the customer requests (or tasks) to the VMs of the clouds such that the overall completion time i.e., makespan is minimized. Moreover, the algorithm introduces a new phase called allocating to reschedule the tasks to meet the requirement of scheduling strategy. We perform rigorous experiments on benchmark as well as synthetic datasets and compare the experimental results by extending two existing multi-cloud scheduling algorithms as per the proposed model. The results clearly indicate that the proposed algorithm outperforms both the algorithms in terms of makespan and average cloud utilization.

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

“…and providing the desired level of quality of service (QoS) [5,[12][13][14][15]. Wu et al [13] proposed a task scheduling algorithm based on several quality of service (QoS) metrics.…”

“…Their algorithm uses diversity detection and improvement detection operators to dynamically select the most suitable heuristic from a pool of heuristics to find better candidate solutions. Other well-known metaheuristics such as Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Bat algorithm have also been used to schedule tasks on cloud systems [15,[34][35][36].…”

“…However, if the schedule violates any of the constraints, then one or more tasks should be allocated and scheduled on other virtual machines in such a way that none of the constraints are violated. Therefore, the proposed algorithm scans the generated schedule to determine the tasks that do not meet their deadlines (lines 9-11), and relocates and reschedules them on virtual machines that incur the minimum cost provided that the rescheduled tasks meet their deadlines on the selected VMs (lines [12][13][14][15][16][17]. The overall complexity of the algorithm is O(n 2 m).…”

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

“…Allocation-aware task scheduling (ATS) algorithm is proposed by Sanjaya K Panda for multi-cloud environment. 48 Each cloud consists of different datasets to deploy VMs. The proposed model comprises three steps, that is, matching, allocating, and scheduling.…”

“…Deniziak et al 55 analyzed the problem of cloud RA to minimize execution cost in RT applications. The existing methodologies, that is, iterative improvement 48 and constrained logic programming, 49 briefly explained and resolved the application cost problem, but still there is a chance of improvement to minimize the execution cost. An efficient genetic procedure is proposed to minimize the cost of applications with high QoS in a cloud computing environment.…”