Abstract
An important issue in cloud computing is the balanced flow of big data centers, which usually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data flow distributions that can take into account the possible change of states in the network. A number of scheduling techniques for achieving load balancing have therefore been proposed. To the best of my knowledge, there is no tool that can be used independently for different algorithms, in order to model the proposed system (network topology, linking and scheduling algorithm) and use its own probability-based parameters to test it for good balancing and scheduling performance. In this paper, a new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with a number of techniques to test the most important parameters that determine good load balancing and scheduling performance in the network. In this work, ProMo is only used for testing with two well-known dynamic data flow scheduling schemes, and the experimental results verify the fact that it is indeed suitable for testing the performance of load-balanced scheduling algorithms.
Highlights
One of the main challenges in today’s networking is the efficient data flow scheduling among the numerous servers that constitute a data center
The static flow scheduling approaches become rather unsuitable for big data flows
Tang et al [8] proposed a Dynamical Load-Balanced Scheduling (DLBS) approach for maximizing the network throughput and balancing the network load. They developed a set of efficient heuristic scheduling algorithms for the two typical OpenFlow network models, which balance data flows at regular time intervals
Summary
One of the main challenges in today’s networking is the efficient data flow scheduling among the numerous servers that constitute a data center. Dynamic load-balanced scheduling is the task of distributing as evenly as possible the traffic within a network (or among its links), while keeping in mind the overall network state at a time, or, at least, at specified time intervals. It is important to take into consideration the dynamic network changes; in other words, it is important to consider the state of the network This is due to the fact that while new data flows keep arriving at different (or even similar) rates between data servers, some links may collapse and become unavailable for some time [11], meaning that some data flows may need to be re-transmitted to a different server or servers, changing the amount of load being stored and processed by each machine. A probabilistic model of dynamic load-balanced data flows is developed.
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