Constraint aware profit maximization scheduling of tasks in heterogeneous datacenters
- PDF / 1,358,221 Bytes
- 27 Pages / 439.37 x 666.142 pts Page_size
- 72 Downloads / 166 Views
Constraint aware profit maximization scheduling of tasks in heterogeneous datacenters Chinmaya Kumar Swain1
· Bhawana Gupta1 · Aryabartta Sahu1
Received: 16 March 2020 / Accepted: 5 August 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The extensive use of cloud services in different domains triggers the efficient use of cloud resources to achieve maximum profit. The heterogeneous nature of data centers and the heterogeneous resource requirement of user applications create a scope of improvement in task scheduling. The resource requirements in terms of task constraints must be fulfilled for the tasks to be admitted to the system. Once a task admitted to the system, it may violate service level agreement and incurs penalty due to the disproportionate resource allocation at run time. The latency-sensitive and short-lived workloads need effective scheduling to gain more profit. In this work, we propose Heuristic of Ordering and Mapping for Constraint Aware Profit Maximization (HOMCAPM) problem for efficient scheduling of tasks with constraints and deadlines to gain maximum profit. The HOM-CAPM approach considers estimation of task execution time in a heterogeneous environment, efficient task ordering, and profit-based task allocation to maximize the overall profit of the cloud system. To gain maximum profit the proposed heuristic considers two cases, (a) not allowing the tasks for execution if it expected to miss its deadline and (b) allowing the task which earns substantial profit even though it is expected to miss its deadline. The results of the extensive simulation using Google trace data as input show that our proposed HOM-CAPM approach generates more profit than other state-of-the-art approaches. Keywords Datacenter · Cloud computing · Scheduling · Constraint · Profit Mathematics Subject Classification 68T20 · 68W40 · 68Q15 · 97K50
B
Chinmaya Kumar Swain [email protected] Bhawana Gupta [email protected] Aryabartta Sahu [email protected]
1
Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India
123
C. K. Swain et al.
1 Introduction Over the years, cloud computing gains popularity due to its unique properties like flexibility, elasticity, availability of unlimited computational resources, and pay-asyou-use pricing model [1,2]. This induces many clients to transfer their business to the cloud. The key feature of cloud computing is pay-as-you-use, which means the users need to pay for the resources they consume for the entire time of usage [3,4]. According to the present practices of different cloud service providers (Amazon EC2, Microsoft Azure, etc.), the customers’ demand for CPU, memory, etc., are provided to the customer in terms of virtual machines (VMs) using virtualization technology. In the virtualization method, flexibility is higher as any requirement of VM in terms of OS, kernel, library, and architecture, etc can be provided on top of the host machines. But the overhead associated with virtualization is a
Data Loading...
