Abstract
As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.
Highlights
Cloud computing is a large-scale distributed computing paradigm in which a pool of computing resources, e.g., computation, storage and networking, is available to cloud users via the Internet [1, 2]
This paper focuses on the virtual machine (VM) scheduling problem, i.e., how to determine the allocations of VM requests to computing nodes, taking into account the quality of service (QoS) guarantees, as well as the incentives for both the cloud users and the cloud provider
For each computing node CNj (CNj∈QCSi), if CNj is the computing node that executes VM request V i, the price of CNj increases by a increasing coefficient α, which is a decimal slightly greater than 1, to avoid CNj always being selected in the following steps; otherwise, the price of CNj decreases by a decreasing coefficient β, which is a decimal slightly less than 1, to avoid CNj never being selected in the following steps
Summary
Cloud computing is a large-scale distributed computing paradigm in which a pool of computing resources, e.g., computation, storage and networking, is available to cloud users via the Internet [1, 2]. How to allocate VMs to suitable physical machines according to cloud users’ QoS requirements is the VM scheduling problem studied in this paper. From the perspective of cloud users, there are two major concerns in VM scheduling, i.e., successful execution rate of the VM requests (SERoV) (e.g., [11,12,13,14]), and the combined cost (which is the total execution cost of all users’ job requests) incurred (e.g., [3, 7, 11, 15]). This paper, in contrast, makes an endeavor to investigate the VM scheduling problem in cloud computing by addressing the major incentives for both parties, i.e., maximizing the SERoV and minimizing the combined cost (incentives for cloud users), and at the same time minimizing the FDoP (incentive for cloud provider).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
