Abstract
In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.
Highlights
Cloud computing has risen as a new computing paradigm that brings unparalleled flexibility and access to shared and scalable computing resources
We show the impact of MESF on the energy consumed by a data center, and compare it to that of a data center that assigns tasks to servers randomly [7]
In this paper, we formulated the task assignment for a data center as an integer programming optimization problem and proved the average task response time is bounded with an optimized number of active servers
Summary
Cloud computing has risen as a new computing paradigm that brings unparalleled flexibility and access to shared and scalable computing resources. Task scheduling and energy consumption A data center is required to handle a large number of tasks demanding different computational resources, e.g. CPU, memory, and communications Under this variety, servers may provide different response times and consume different levels of energy for different types of tasks. When the data center has a light load and there is no backlogged tasks in the queues at the servers, the Analysis of homogeneous tasks In the remainder of this section, we analyze the assignment of a single type of tasks (V = 1) and estimate a bound of the number of servers required to comply with the maximum response time. The simulation results show the proposed MESF scheme achieves minimum average task response time, which is bounded by the capacities of the queues, and at the same time, minimum energy consumption for a given number of servers, M. The larger number of servers used by the random-based scheme results in larger energy consumption and resource over-provisioning
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