1 Scalable Service Migration in Autonomic Network Environments Konstantinos Oikonomou, Member, IEEE, and Ioannis Stavrakakis, Fellow, IEEE Abstract— Service placement is a key problem in communica- tion networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large opti- mization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more commu- nication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and – under certain conditions – the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies. Index Terms— Service Placement, Service Migration, Auto- nomic Networks, Scalability. I. I NTRODUCTION I NTERNET globalization and expansion make the service placement problem a challenging one and necessitate a careful selection of the location of the service facilities (a facility being a service provisioning infrastructure), aiming at bringing the service provision points close to the demand in order to minimize communication costs (i.e., resource consumption) and enhance the Quality of Service (QoS) of the provided service. Due to the recent technological changes (e.g., powerful machines and services have proliferated), the traditional problem of placing relatively few big services in one of the few (powerful) potential service provider facilities (big network elements) is increasingly being transformed into a problem of placing the one or more service facilities in one of the numerous network nodes that are now capable of hosting services. Peer-to-peer networks, cloud computing, content dis- This work has been supported in part by the IST-FIRE project Autonomic Network Architecture (ANA) (IST-27489) and the IST-FET project SOCIAL- NETS (IST-217141), funded by the European Commission. Konstantinos Oikonomou is with the Department of Informatics, Ionian University, Corfu, Greece. Address: Tsirigori Square 7, 49100 Corfu, Greece. Phone: +30 26610 87708, Fax: +30 26610 48491, E-mail: okon@ionio.gr. Ioannis Stavrakakis is with the Department of Informatics and Telecommu- nications, National and Kapodistrian University of Athens, Athens, Greece. Address: Panepistimiopolis, Ilissia, 157 84, Athens, Greece. Phone: +30 210 7275315, Fax: +30 210 7275333, E-mail: ioannis@di.uoa.gr. tributions networks, software updates and patches and sensor networks are examples of such modern environments. The problem of determining the optimal service placement has been studied in the past in areas such as transportation and supply networks [1], and has been approached through the formulation and solution of large optimization problems (NP -hard) requiring global knowledge, as for instance is the case with the k-median problem [2]. Such approaches requiring global knowledge and a continuous recalculation of the solution in case of network changes; do not scale and are not suitable for dynamic network environments, such as those considered in this work. Instead, approaches based on local information should be adopted, despite the fact that they might not be able to guarantee optimality all the time (near-optimal solutions). In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. The approach advocated in this paper – parts of it initially presented in [3] and [4] – is that of moving a service facility among neighbor nodes by utilizing local information, in a such way that the cost of service provision is reduced and the service facility reaches under certain conditions the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity. The first proposed policy (referred to as Migration Policy S) is analytically shown to be capable of moving the facilities along a monotonically cost decreasing path in the network. For special cases of topologies such as trees, it is analytically shown that under Migration Policy S a single facility moves until it reaches the optimal position (i.e., the node at which the overall cost is minimized). In the general case, service facility migration under Migration Policy S, allows for overall cost reduction, but it may fail to move the facilities until the end of a monotonically cost decreasing path, mostly due to unforeseen shortcuts (i.e., alternative shortest paths utilized by some nodes to reach a certain facility after a facility movement). The potential cost reduction that is due to the aforementioned shortcuts is not taken into account under Migration Policy S, thus certain facility movements that would allow for further cost reduction are not detected and thus not implemented. The aforementioned limitation is overcome in the case of a single facility and for topologies of equal link weights, while still utilizing only local information. Note that topologies with equal link weights and a single service facility are not uncommon. For such environments, a new policy (referred to