An Efficient Intra-Server and Inter-Server Load Balancing Algorithm for Internet Distributed Systems

Abstract

The growing popularity of Internet Distributed System has drawn enormous attention in business and research communities for handling large number of client requests. These requests are managed by a set of servers. However, the requests may not be equally distributed due to their random nature of arrivals. The optimal assignment of the requests to the servers is a well-known NP-hard problem. Therefore, many algorithms have been proposed to address this problem. However, these algorithms suffer from an excessive number of comparisons. In this paper, a Swapping-based Intra-and inter-Server (SIS) load balancing with padding algorithm is proposed for its solution. The algorithm undergoes a three-phase process to balance the loads among the servers. The proposed algorithm is compared with a client-server load balancing algorithm and the performance is measured in terms of the number of load comparisons and load factor. The simulation outcomes show the efficacy of the proposed algorithm.

An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment

“…Various load balancing algorithms are used in cloud computing systems, such as round-robin [32], Opportunistic Load Balancing Algorithm (OLB) [33], Max-Min Algorithm [34], Ant Colony Optimization [35], Generalized Priority Algorithm [36], Join-Idle-Queue [37], GA-based load balancing [38], Stochastic Hill Climbing Technique [39], Decentralized Content-Aware Load Balancing [40], Server-Load Balancing for Internet Distributed Services [41], and other techniques [36][37][38]. The various optimization techniques are based on their problem statement and key objectives in Table 1.…”

Efficient Fault Tolerant Algorithms for Internet Distributed Systems

“…Recently, Panda, Mishra and Das (2017) have developed a swapping-based intra-and interserver load balancing with padding algorithm to balance the loads among the servers. This algorithm minimizes the number of load comparisons and maximizes the load factor.…”

“…In this paper, the same problem as shown in (Nishida, & Nguyen, 2013;Bharati, Naidu, Kiran, Khune, & Vyas, 2014;Panda, Mishra, & Das, 2017) is presented. In addition, this paper formulates a fault tolerance problem and presents four different algorithms to handle server, client, client-server and connection failures.…”

“…In addition, this paper formulates a fault tolerance problem and presents four different algorithms to handle server, client, client-server and connection failures. The complexity of the proposed algorithms is also same as (Bharati, Naidu, Kiran, Khune, & Vyas, 2014;Panda, Mishra, & Das, 2017). However, the proposed algorithms consider various failures which are not considered in (Nishida, & Nguyen, 2013;Bharati, Naidu, Kiran, Khune, & Vyas, 2014;Panda, Mishra, & Das, 2017).…”

“…The complexity of the proposed algorithms is also same as (Bharati, Naidu, Kiran, Khune, & Vyas, 2014;Panda, Mishra, & Das, 2017). However, the proposed algorithms consider various failures which are not considered in (Nishida, & Nguyen, 2013;Bharati, Naidu, Kiran, Khune, & Vyas, 2014;Panda, Mishra, & Das, 2017). The proposed algorithms are simulated extensively and compared to show the effectiveness of the proposed algorithms.…”