As the Software Defined Networking (SDN) paradigm gains momentum, every network operator faces the obvious dilemma: when and how to migrate from existing IP routers to SDN compliant equipments. A single step complete overhaul of a fully functional network is impractical, while at the same time, the immediate benefits of SDN are obvious. A viable solution is thus a gradual migration over time, where questions of which routers should migrate first, and whether the order of migration makes a difference, can be analyzed from techno economic and traffic engineering perspective. In this paper, we address these questions from the techno economic perspective, and establish the importance of migration scheduling. We propose optimization techniques and greedy algorithms to plan an effective migration schedule, based on various techno economic aspects, such as technological gains in combinations with CapEx limitations. We demonstrate the importance of an effective migration sequence through two relevant network management metrics, namely, number of alternative paths availed by a node on migration, and network capacity savings. Our results suggest that the sequence of migration plays a vital role, especially in the early stages of network migration to SDN.
Network migration has been studied using and models. In the system dynamics approach, the migration problem is treated as a dynamic system @cite_13 , where the rate of migration depends on number of migrated agents in the system. On the other hand, in an agent-based approach @cite_9 , the system consists of an ensemble of agents, each trying to increase its own utility. Such studies have mostly been conducted for IPv6 @cite_11 and secure BGP @cite_6 .
@cite_9 @cite_13 @cite_6 @cite_11
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The rate of deployment and adoption issues of new network technologies, IPv6 in particular, have recently been hotly debated in the research community. However, the question of how protocols migrate, especially the dynamics of migration, to new paradigms is still largely open. In this paper, we address the issue from a game theoretic point of view. We model and analyze the profit maximizing strategies of Autonomous Systems (ASes); both the properties of ASes and the topology of the Internet is considered. The contribution of our work is threefold. First, we propose an economic model of the ASes and their relations from the IPv4-IPv6 migration viewpoint. Second, we apply the findings of evolutionary dynamics on the problem of migration by incorporating Internet-specific properties to the evolutionary model, namely the size of the ASes and the cost of migration. The analyses show that even if IPv6 has higher payoff than IPv4, the whole migration does not happen always fast. Finally, extensive simulations are carried out based on the proposed models to illustrate the impacts of different parameters on the IPv6 migration dynamics in realistic scenarios. Conventionally, network migration models study competition between emerging and incumbent technologies by considering the revenue increase and migration cost. We propose to extend the existing network migration models with new critical factors, including (i) synergistic relationships across multiple technologies, (ii) reduction in operation expenditures (OpEx), and, (iii) effect of social factors on human decisions. To this end, we propose a novel agent-based migration model considering these factors. Based on the model, we analyze the case study of optimal path computation with joint migration to two emerging networking paradigms, i.e., IETF Path Computation Element (PCE) and Software-Defined Networking (SDN). Our results demonstrate the synergistic effects of migration to multiple complementary technologies, and shows that a technology migration may be eased by the joint migration to multiple technologies. The incredible growth of the social web over the last decade has ushered in a flurry of new social media sites. On one hand, users have an inordinate number of choices; on the other hand, users are constrained by limited time and resources and have to choose sites in order to remain social and active. Hence, dynamic social media entails user migration, a well studied phenomenon in fields such as sociology and psychology. Users are valuable assets for social media sites as they help contribute to the growth of a site and generate revenue by increased traffic. We are intrigued to know if social media user migration can be studied, and what migration patterns are. In particular, we investigate whether people migrate, and if they do, how they migrate. We formalize site and attention migration to help identify the migration between popular social media sites and determine clear patterns of migration between sites. This work suggests a feasible way to study migration patterns in social media. The discovered patterns can help understand social media sites and gauge their popularity to improve business intelligence and revenue generation through the retention of users. In mobile agent systems, program code together with some process state can autonomously migrate to new hosts. Despite its many practical benefits, mobile agent technology results in significant new security threats from malicious agents and hosts. In this paper, we propose a security architecture to achieve three goals: certification that a server has the authority to execute an agent on behalf of its sender; flexible selection of privileges, so that an agent arriving at a server may be given the privileges necessary to carry out the task for which it has come to the server; and state appraisal, to ensure that an agent has not become malicious as a consequence of alterations to its state. The architecture models the trust relations between the principals of mobile agent systems and includes authentication and authorization mechanisms. Today's trend toward network virtualization and software-defined networking enables flexible new distributed systems where resources can be dynamically allocated and migrated to locations where they are most useful. This paper proposes a competitive analysis approach to design and reason about online algorithms that find a good tradeoff between the benefits and costs of a migratable service. A competitive online algorithm provides worst-case performance guarantees under any demand dynamics, and without any information or statistical assumptions on the demand in the future. This is attractive especially in scenarios where the demand is hard to predict and can be subject to unexpected events. As a case study, we describe a service (e.g., an SAP server or a gaming application) that uses network virtualization to improve the quality of service (QoS) experienced by thin client applications running on mobile devices. By decoupling the service from the underlying resource infrastructure, it can be migrated closer to the current client locations while taking into account migration costs. We identify the major cost factors in such a system and formalize the wide-area service migration problem. Our main contributions are a randomized and a deterministic online algorithm that achieve a competitive ratio of @math in a simplified scenario, where @math is the size of the substrate network. This is almost optimal. We complement our worst-case analysis with simulations in different specific scenarios and also sketch a migration demonstrator. Elastic scaling and load balancing with efficient switch migration are critical to enable the elasticity of software-defined networking (SDN) controllers, but learning how to improve migration efficiency remains a difficult problem. To address this issue, a switch migration-based decision-making (SMDM) scheme is put forward that could be made aware of the load imbalance by a switch migration trigger metric; the migration efficiency model for this scheme is built to make a tradeoff between migration costs and the load balance rate. An efficiency-aware switch migration algorithm based on greedy method is designed to utilize the migration efficiency model and thus guide the choice of possible migration actions. We implement a proof of the scheme and present a numerical evaluation using Mininet emulator to demonstrate the effectiveness of our proposal. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. This paper is concerned with the page migration (or file migration) problem (Black and Sleator, Technical Report CMU-CS-89-201, Department of Computer Science, Carnegie-Mellon University, 1989) as part of a large class of on-line problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is accessed by different processors over time. The page is allowed to be migrated between processors. However a migration incurs higher communication cost than an access (proportionally to the page size). The problem is that of deciding when and where to migrate the page in order to lower access costs. A more general setting is the k-page migration problem where we wish to maintain k copies of the page. The page migration problems are concerned with a dilemma common to many on-line problems: determining when it is beneficial to make configuration changes. We deal with the relaxed task systems model which captures a large class of problems of this type, that can be described as the generalization of some original task system problem (, J. ACM 39(4) (1992) 745-763). Given a c-competitive algorithm for a task system we show how to obtain a deterministic O(c2) and randomized O(c) competitive algorithms for the corresponding relaxed task system. The result implies deterministic algorithms for k-page migration by using k-server (, J. Algorithms 11(2) (1990) 208-230) algorithms, and for network leasing by using generalized Steiner tree algorithms (, Proc 7th Ann. ACM-SIAM Symp. on Discrete Algorithms, January 1996, pp. 68-74), as well as providing solutions for natural generalizations of other problems (e.g. storage rearrangement (, Proc. 36th Ann. IEEE Symp. on Foundations of Computer Science, October 1995, pp. 392-403)). We further study some special cases of the k-page migration problem and get optimal deterministic algorithms. For the classical page migration problem we present a deterministic algorithm that achieves a competitive ratio of 4:086, improving upon the previously best competitive ratio of 7 (, Proc. 25th ACM Symp. on Theory of Computing, May 1993, pp. 164-173). (The current lower bound on the problem is 3:148 (, J. Algorithms 24(1) (1997) 124-157). Copyright 2001 Elsevier Science B.V. In this paper, we tackle challenges in migrating enterprise services into hybrid cloud-based deployments, where enterprise operations are partly hosted on-premise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloud-based architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multi-tiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprise-specific constraints, cost savings, and increased transaction delays and wide-area communication costs that may result from the migration. Evaluations based on real enterprise applications and Azure-based cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios. This paper presents a generic meta-model of multi-agent systems based on organizational concepts such as groups, roles and structures. This model, called AALAADIN, defines a very simple description of coordination and negotiation schemes through multi-agent systems. Aalaadin is a meta-model of artificial organization by which one can build multi-agent systems with different forms of organizations such as market-like and hierarchical organizations. We show that this meta-model allows for agent heterogeneity in languages, applications and architectures. We also introduce the concept of organizational reflection which uses the same conceptual model to describe system level tasks such as remote communication and migration of agents. Finally, we briefly describe a platform, called MADKIT, based on this model. It relies on a minimal agent kernel with platform-level services implemented as agents, groups and roles. This paper describes the basic concepts of the network migration problem, as well as its cost optimal solution with an ant colony algorithm. Generally there are many possible approaches to calculate network migration strategies. One of those is to consider the migration as a Shortest Path Problem, which can be solved using meta heuristics. In this paper it will be analyzed how ant colony optimization can be used to calculate migration sequences with minimized costs. Results of our investigation are information about the speed of convergence of ant colony algorithms towards an optimal cost value. The used resource utilization to cover increasing network demands will be presented also. We study complexity and algorithms for network updates in the setting of Software Defined Networks. Our focus lies on consistent updates for the case of updating forwarding rules in a loop free manner and the migration of flows without congestion. In both cases, we study how the power of two affects the respective problem setting. For loop freedom, we show that scheduling consistent updates for two destinations is NP-hard for a sublinear number of rounds. We also consider the dynamic case, and show that this problem is NP-hard as well via a reduction from Feedback Arc Set. While the power of two increases the complexity for loop freedom, the converse is true when allowing to split flows twice. For the NP-hard problem of consistently migrating unsplittable flows to new routes while respecting waypointing and service chains, we prove that two-splittability allows the problem to be tractable again. Checkpointing of parallel applications can be used as the core technology to provide process migration. Both checkpointing and migration, are an important issue for parallel applications on networks of workstations. The CoCheck environment which we present in this paper introduces a new approach to provide checkpointing and migration for parallel applications. CoCheck sits on top of the message passing library and achieves consistency at a level above the message passing system. It uses an existing single process checkpointer which is available for a wide range of systems. Hence, CoCheck can be easily adapted to both, different message passing systems and new machines. International migration is one of the major determinants of demographic change. Although efforts to produce comparable statistics are underway, estimates of demographic flows are inexistent, outdated, or largely inconsistent, for most countries. We estimate age and gender-specific migration rates using data extracted from a large sample of Yahoo! e-mail messages. Self-reported age and gender of anonymized e-mail users were linked to the geographic locations (mapped from IP addresses) from where users sent e-mail messages over time (2009-2011). The users' country of residence over time was inferred as the one from where most e-mail messages were sent. Our estimates of age profiles of migration are qualitatively consistent with existing administrative data sources. Selection bias generates uncertainty for estimates at one point in time, especially for developing countries. However, our approach allows us to compare in a reliable way migration trends of females and males. We document the recent increase in human mobility and we observe that female mobility has been increasing at a faster pace. Our findings suggest that e-mail data may complement existing migration data, resolve inconsistencies arising from different definitions of migration, and provide new and rich information on mobility patterns and social networks of migrants. The use of digital records for demographic research has the potential to become particularly important for developing countries, where the diffusion of Internet will be faster than the development of mature demographic registration systems. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article. At least three factors in the existing migration frameworks make them less suitable in Grid systems especially when the goal is to improve the response times for individual applications. These factors are the separate policies for suspension and migration of executing applications employed by these migration frameworks, the use of pre-defined conditions for suspension and migration and the lack of knowledge of the remaining execution time of the applications. In this paper we describe a migration framework for performance oriented Grid systems that implements tightly coupled policies for both suspension and migration of executing applications and takes into account both system load and application characteristics. The main goal of our migration framework is to improve the response times for individual applications. We also present some results that demonstrate the usefulness of our migration framework. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Server consolidation is important for better resource utilization and efficient energy saving for cloud datacenters which host thousands of virtual machines (VMs) to support multitenant applications. The typical approach is to migrate VMs and reallocate workload among different servers in a way to minimize the total number of servers used. However, most existing works for server consolidation focus mainly on how to reduce the number of active servers and do not account for the migration overhead incurred to the applications on the migrating VMs (such as downtime). In this paper, we propose an adaptive mechanism to schedule VM allocation in cloud datacenters. Our solution takes into account the resource utilization and migration overheads, and adaptively allocates each VM to servers based on the estimated saturation level. As a result, the quality and overhead of consolidation is balanced and the total cost is minimized. The simulation results show that our mechanism could increase the average utilization on servers by up to 97 while reducing the total migration cost by about 60 , as compared with existing solutions.