# Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment
## 1 Introduction  
The maturation of the recent cloud-computing paradigm has enabled the construction of flexible network services that are not bound to hardware construction. Elasticity is one of the key features of cloud-hosted services built on virtualization technology. Elasticity is the ability to change the size of network services flexibly as necessary. The control methods of cloud-hosted services and services that have elasticity are referred to as elastic services or elastic resource management [1; 2; 3]. To utilize the elasticity of cloud environments, administrators should accurately capture the operational status of server systems, which changes constantly according to service requests incoming irregularly. Currently, various studies are being conducted in many domains, from the analysis of the theoretical model to the observation and control of the operating characteristics of the actual system. However, there are no useful methods or functions to know that the behavior properties of server systems have changed to abnormal ones, before the operational status of server system that administrators observe, changes drastically. For example, quality of service (QoS) measures, such as throughput and response time, are widely used to capture the operational status of a running server system. When the degradation of the QoS of operating services is detected, it can be assumed that the system is overloaded, and a control operation for resolving the fault should be applied. However, such changes in the QoS are detected only after a scenario in which a problem in the server system has already occurred, such as overload, resource exhaustion, or failure. Therefore, it is difficult to detect and avoid in advance that operating services are falling into such an undesirable state. Hence, to realize stable and continuous service operation, new approaches for detecting that the system is fallinginto an undesirable state are required based on the observation of the operational states of server systems. In this paper, we focus on the management of server systems that include cloud systems, and propose a new method for detecting the sign of undesirable situations before the system becomes overloaded as a result of various causes. In this method, a measure that utilizes the fluctuation of the macroscopic operational state observed in the server system is introduced. The proposed measure has the property of drastically increasing before the server system is in an undesirable state, that is, before server performance declines. Accordingly, using the proposed measure, we realize the function to detect that the server system is falling into an overload scenario, and we demonstrate the effectiveness of the proposed measure through evaluation experiments.  
The main contribution of this paper is to propose the method that solves the problem related to the acquisition of the fluctuating operational state of server systems which provide various services over the distributed computing environment such as cloud-hosted services. The proposed measure aims to provide a simple and easy-to-use facility for detecting abnormal states of server systems.  
In Section 2, we describe the background of this paper and the problems of interest. In Section 3, based on a theoretical analysis of the operation of a general server system that includes a cloud system, we propose a new observation measure, which is, the variance of the fluctuation of the macroscopic operating state of the system. Then, in Section 4, we demonstrate the effectiveness of the proposed measure through an evaluation experiment using an experimental server system. In Section 5, we present the conclusions and future work.