Maximizing Performance: Expert Strategies for Network and Systems Management in the Cloud Era

Abstract

Cloud computing is an advanced mode of network computation and data processing that is gaining widespread popularity in today’s era. With the spread of large data centers that host cloud applications, there has been a rapid increase in energy usage. Studies indicate that a significant amount of the high energy usage in data centers is due to providing more network resources than necessary to accommodate peak demand. This paper suggests a resolution to the issue by developing a dynamic and energy-saving approach to managing resources. To conserve energy and ensure cloud users receive excellent service, the plan introduces a multitier cloud structure that divides physical machines (PMs) into two groups: a hot pool (active, running) and a warm pool (on standby in dynamic sleep mode). Every PM has a resource search engine (RSE) to locate an unused virtual machine (VM) for the request, and a synchronous sleep mechanism is added to the warm pool. To assess the overall performance of the cloud system's service with the suggested approach, a combined queueing system is set up. This system consists of three probabilistic submodels and is solved using a matrix-geometric method. As a result, the system's energy-saving rate and the average latency of requests are calculated. The paper will use numerical data to demonstrate how the synchronous sleep mechanism impacts system performance.