Abstract-Cloud service providers offer their customers to rent or release hardware resources (CPU, RAM, HDD), which are isolated in virtual machine instances, on demand. Increased load on customer applications or web services require more resources than a physical server can supply, which enforces the cloud provider to implement some load balancing technique in order to scatter the load among several virtual or physical servers. Many load balancers exist, both centralized and distributed, with various techniques. In this paper we present a new solution for a low level load balancer (L3B), working on a network level of OSI model. When a network packet arrives, its header is altered in order to forward to some end-point server. After the server replies, the packet's header is also changed using the previously stored mapping and forwarded to the client. Unfortunately, the results of the experiments showed that this implementation did not provide the expected results, i.e., to achieve linear speedup when more server nodes are added.

Cloud, fog and dew computing concepts offer elastic resources that can serve scalable services. These resources can be scaled horizontally or vertically. The former is more powerful, which increases the number of same machines (scaled out) to retain the performance of the service. However, this scaling is tightly connected with the existence of a balancer in front of the scaled resources that will balance the load among the end points. In this paper, we present a successful implementation of a scalable low-level load balancer, implemented on the network layer. The scalability is tested by a series of experiments for a small scale servers providing services in the range of dew computing services. The experiments showed that it adds small latency of several milliseconds and thus it slightly reduces the performance when the distributed system is underutilized. However, the results show that the balancer achieves even a super-linear speedup (speedup greater than the number of scaled resources) for a greater load. The paper discusses also many other benefits that the balancer provides.

Cloud computing paradigm offers instantiating and deactivating the virtual machine instances on demand according to the clients requirements. When some customer's application or a service needs more resources than a physical server can supply, the cloud provider offers a certain load balancing technique to distribute the load among several servers that will host the analyzed application or service. Additionally, the cloud provider should offer a resource broker to make the application scalable and elastic. In this paper we present a new solution for a low level load balancer, working on a network level. Our load balancer maps the memory addresses of the balancer and the target physical servers (or virtual machines on the cloud) and thus balances the load. The experiments showed that it adds small latency of several milliseconds and thus it slightly reduces the performance when the distributed system is underutilized. However, there is a region of client requests where the system achieves a superlinear speedup (speedup greater than the number of scaled resources). Our case study with doubled resources achieves up to 6.5 speedup (maximum expected linear speedup is 2).