Policy based resource allocation in IaaS cloud
Highlights
► For scheduling a deadline sensitive lease, we suggest use of multiple slots. ► We apply two concepts: swapping and backfilling in addition to preemption. ► It can reschedule already accommodated leases to make space for a newly arrived lease. ► Use of swapping and multiple slots increases the number of accepted leases. ► Results indicate that the proposed algorithm requires rescheduling of fewer leases.
Introduction
Cloud provides a managed pool of resources which includes storage, compute power and software services. Cloud provides scalability using virtualization and host applications which suffer high load at certain times. Resources provided to an application can be reconfigured to adjust to a variable load. In IaaS cloud the resources (compute capacity and storage) are provided in the form of virtual machines to users. To optimize resource utilization on cloud provider’s side, it is necessary to handle the following two things:
- 1.
Where to place newly created virtual machine?
- 2.
When to dispatch newly created virtual machine to a particular physical machine?
The rest of this paper is organized as follows. Section 2 describes resource allocation on cloud. In Section 3, we discuss the task model, system requirements, proposed algorithm to provide support for deadline sensitive requests and modifications to the existing algorithm of Haizea. In Section 4, we have discussed the experiments and results obtained by comparing the modified algorithm with the existing one. Section 5 contains discussion on related work. We conclude this paper in Section 6 with future enhancements.
Section snippets
Resource Allocation on Cloud
IaaS cloud allocates resources to competing requests based on pre-defined resource allocation policies. Presently, most of the cloud providers rely on simple resource allocation policies like immediate and best effort [1]. Comparison of various cloud toolkits and public clouds based on resource allocation policies and VM placement policies is given in Table 1 [1]. Amazon EC2[2] is a public cloud which provides computing resources to general public on pay-per-use model. Nimbus [3], Eucalyptus [4]
System description
To request resources from Haizea, consumer has to submit lease in a specific format. Main parameters in this request are number of virtual nodes, amount of physical resources for each node, start time, duration and deadline. CPU and memory are considered to be two computational resources, which can be requested by a user. It also has a software field, where a user can specify the software to be deployed on allotted resources. In this paper, only those leases are considered which demand for full
Experiments and results
In order to evaluate the performance of proposed algorithm to schedule deadline sensitive leases, it is compared with the existing algorithm of Haizea to schedule deadline sensitive leases. From now we will refer the existing algorithm of Haizea to schedule deadline sensitive leases as existing algorithm. The existing algorithm considers finding single slot to schedule deadline sensitive leases and only preemption to reschedule deadline sensitive leases. In addition to the methods used by
Related work
In multiprocessor systems we have the problem of when and where to place the task. Here, if uni-processor real time scheduling algorithm such as earliest deadline first [10], [11] or rate monotonic algorithm [12] are used then it will give low performance. Mainly two strategies are there to handle scheduling on multiprocessor. They are partitioning and non-partitioning (global) [13], [14]. In partitioning, each task will be allocated to a processor based on bin packing algorithm. This task then
Conclusion and future work
The proposed algorithm finds multiple slots in addition to finding single slot while scheduling a deadline sensitive lease. It also applies two concepts (swapping and backfilling) in addition to preemption, while rescheduling already accommodated leases to make space for a newly arrived lease. Swapping uses the information available about leases to be rescheduled, to decide the order in which they should be rescheduled. When swapping and preemption both fails to schedule a lease, the proposed
Acknowledgment
The authors would like to thank Dr. Srikrishnan Divakaran, DA-IICT for suggestions and views given during this work.
Amit Nathani completed M.Tech (ICT) from Dhirubhai Ambani Institute of Information and Communication Technology in the year 2010. He is working as a software developer.
References (24)
- et al.
An Open Source Solution for Virtual Infrastructure Management in Private and Hybrid Clouds
(2009) - Amazon EC2,...
- Nimbus,...
- D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, D. Zagorodnov, The eucalyptus open-source...
- et al.
Overhead Matters: A Model for Virtual Resource Management
(2006) - B. Sotomayor, R. Montero, I. Llorente, I. Foster, Resource leasing and the art of suspending virtual machines, in: IEEE...
- B. Sotomayor, R.S. Montero, I.M. Llorente, I. Foster, Capacity leasing in cloud systems using the opennebula engine,...
- B. Sotomayor, K. Keahey, I. Foster, Combining Batch Execution and Leasing Using Virtual Machines, in: HPDC, Manchester,...
- U. Farooq, S. Majumdar, E.W. Parsons, Efficiently Scheduling Advance Reservations in Grids, Technical Report SCE-05-14,...
- et al.
Deadline Scheduling for Real-Time Systems: EDF and Related Algorithms
(1998)
Scheduling algorithms for multiprogramming in a hard real-time environment
Journal of the ACM
Cited by (163)
Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence
2022, Engineering Applications of Artificial IntelligenceAn enhanced deadline constraint based task scheduling mechanism for cloud environment
2022, Journal of King Saud University - Computer and Information SciencesA pair-based task scheduling algorithm for cloud computing environment
2022, Journal of King Saud University - Computer and Information SciencesCitation Excerpt :3) The proposed algorithm considers the transfer time to make it a realistic one. However, transfer time is not considered in the existing algorithms (Li et al., 2014; Liu et al., 2015; Gawali and Shinde, 2018; Frank, 2005; Kuhn, 2009; Nathani et al., 2012). 4) Unlike the existing algorithms, we consider a new performance metric, called layover time to evaluate the proposed and existing algorithms.
An improved task scheduling algorithm for conflict resolution in cloud environment
2024, International Journal of Computers and ApplicationsScheduling Slice Requests in 5G Networks
2023, IEEE/ACM Transactions on Networking
Amit Nathani completed M.Tech (ICT) from Dhirubhai Ambani Institute of Information and Communication Technology in the year 2010. He is working as a software developer.
Sanjay Chaudhary is a Professor at Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. His research areas are Distributed Computing, Service-Oriented Computing, Multi-core Architectures Programming, and ICT Applications in Agriculture. He has authored four books and a number of book chapters. He has published a number of research papers in international conferences, workshops and journals. He has served on many programme committees of International conferences and workshops and he is also a member of review committees of leading journals. Three Ph.D. candidates have completed Ph.D. under his supervision.
He holds a doctorate degree in computer science from Gujarat Vidyapeeth. Prior to joining DA-IICT, he worked with Gujarat University. He has worked on various large-scale software development projects for corporate sector, co-operative sector, and government organizations. He is actively involved in various consultancy and enterprise application development projects. He has received research grants from IBM and Microsoft in the area of Innovations and High Performance Computing.
Sanjay Chaudhary has provided his services to committees, formed by Gujarat Government, India. Details about Sanjay Chaudhary are available at the following URL:http://intranet.daiict.ac.in/s˜anjay/.
Gaurav Somani is a faculty member at LNM Institute of Information Technology, Jaipur, India. His research interests include cloud computing, virtualization, ad hoc networks and distributed computing. He has published in many reputed conferences like IEEE CLOUD 2009, IC3 2010, IEEE PDGC 2010. A monograph has been published on his recent works by VDM Publishers on Scheduling and Isolation in Virtualization. More details about Gaurav Somani can be found at his home page http://www.lnmiit.ac.in/People/GauravSomani/Gaurav.aspx.
- 1
Master of Technology student at DA-IICT, Gandhinagar.
- 2
Professor of Computer Science and Engineering at DA-IICT, Gandhinagar.
- 3
Lecturer of Computer Science and Engineering at LNMIIT, Jaipur.