Scheduling in Distributed Computing Environment Using Dynamic Load Balancing

This book illustrates various components of Distributed Computing Environment and the importance of distributed scheduling using Dynamic Load Balancing. It describes load balancing algorithms for better resource utilization, increasing throughput and improving user's response time. Various theoretical concepts, experiments, and examples enable students to understand the process of load balancing in computing cluster and server cluster.
The book is suitable for students of Advance Operating Systems, High Performance Computing, Distributed Computing in B.E., M.C.A., M. Tech. and Ph.D courses.

Autorentext

Dr Priyesh Kanungo is working as a Professor and Senior Systems Engineer in Computer Centre at School of Computer Science and Information Technology, Devi Ahilya Vishwavidyalaya (DAVV), Indore. He received B.E. (Industrial and Production Engg.), M.E. (Computer Engineering) from SGSITS, Indore and M Phil and Ph D. in Computer Engineering. He is having more than 26 years of teaching experience in M Tech, Ph D, M.C.A., M.B.A., B.E. etc. in DAVV, Indore. He has been teaching various subjects like Operating Systems, Systems Programming, Data Structures, DBMS and AI. His main areas of research are Advance Operating Systems Distributed Computing, Grid Computing, and Cloud Computing. He has published around 50 research papers in reputed international journals and conferences (including IEEE, ACM and Springer). He is also UGC expert for Computer Science, Applications and Engineering.

Leseprobe
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Chapter 3.1 PREAMBLE:
As we have seen in previous chapter, DLB is one of the distributed scheduling techniques, used extensively to improve scalability and overall system throughput in the rapidly growing resource intensive distributed applications. It is responsible for task scheduling as well as monitoring load variation in the system. In such distributed applications, uneven process arrival may cause load imbalance, where some nodes are overloaded while some other nodes are idle. DLB technique distributes processing workload as evenly as possible among the nodes in a cluster. This helps in improving response time by minimizing job's execution time, minimizing communication overheads and maximizing resource utilization. It also tries to preserve fairness in individual job execution so that a low priority process should not be overtaken by an arbitrary number of higher priority processes [Kanungo,2002). DLB is realized through process migration. DLB allows cluster of nodes to be used as a cost effective alternative to mainframe computing as well as parallel computing [Amiri,2000]. Dynamic load balancing is also used to balance load in a cluster of web servers deployed by websites for processing clients' requests [Abdelzaher,2000]. In present chapter, we have investigated some important issues like measurement of processing workload for taking process transfer decisions and measurement of performance of load balancing algorithms [Petri,1995].
DLB approach can create additional overhead in collecting system state, analyzing the data, making load balancing decisions and transferring the processes from one node to another [Wilson,1998]. Performance of load balancing is closely related to the process information made available to the load balancer, accuracy of the load measurement and the efficiency with which such information is used. Load balancing yields greater performance improvement when workload is heavy and unbalanced. The index used to measure the load in the system strongly affects the performance of load balancing algorithm. It may not be possible to include all load indices in calculating the load, as this requires collecting and exchanging huge amount of information. This may lead to excessive communication cost. Moreover, a poor load index may cause some process migrations which do not contribute to balancing the load in the system, thereby making the situation worse [Dessel,2004].
Therefore, basic problem associated with DLB algorithms is to identify the parameters, which are to be used for estimation of the load on various processors and making load management decisions dynamically. This load will define the current state of the system. Effective load index measures will minimize the communication cost in the system and will allow the load balancer to take quick decisions about load distribution, thereby improving the effectiveness of the algorithm. In a heterogeneous environment, load indices must be adjusted to make them comparable. For example, to compare nodes with different processing powers, their processor utilizations may be divided by respective processing capabilities. Multiple load indices may also be used for making placement decisions. The load index should be easily computable and correlated to those parameters which are to be optimized e.g. response time [Dalhin,2000].
Thus, even though it is established that load balancing facility is necessary for improving the performance of a distributed system, the important issues like load index selection, performance measurement parameters and quality of algorithm needs to be investigated further and are being considered in this chapter.
3.2 LOAD INFORMATON MANAGEMENT:
Transfer of processes from overloaded nodes to underloaded nodes requires load balancer to collect load information from the nodes continuously. This helps in identifying overloaded