Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
The macroscopic behavior of the TCP congestion avoidance algorithm
ACM SIGCOMM Computer Communication Review
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Scheduling data transfers in a network and the set scheduling problem
Journal of Algorithms
Scheduling deadline-constrained bulk data transfers to minimize network congestion
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Delay tolerant bulk data transfers on the internet
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Understanding data center traffic characteristics
Proceedings of the 1st ACM workshop on Research on enterprise networking
The nature of data center traffic: measurements & analysis
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Planning Large Data Transfers in Institutional Grids
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Optimal Scheduling of Urgent Preemptive Tasks
RTCSA '10 Proceedings of the 2010 IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications
Inter-datacenter bulk transfers with netstitcher
Proceedings of the ACM SIGCOMM 2011 conference
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Cloud service providers (CSP) of today operate multiple data centers, over which they provide resilient infrastructure, data storage and compute services. The links between data centers have very high capacity, and are typically purchased by the CSPs using established billing practices, such as 95-thpercentile billing or average-usage billing. These links are used to serve both client traffic as well as CSP-specific bulk data traffic, such as backup jobs, etc. Past studies have shown a diurnal pattern of traffic over such links. However, CSPs pay for the peak bandwidth, which implies that they are under-utilizing the capacity for which they have paid for. We propose a scheduling framework that considers various classes of jobs that are encountered over such links, and propose GRESE, an algorithm that attempts to minimize overall bandwidth costs to the CSP, by leveraging the flexible nature of the deadlines of these bulk data jobs. We demonstrate the problem is not a simple extension of any well-known scheduling problems, and show how the GRESE algorithm is effective in curtailing CSP bandwidth costs.